A multi-objective strategy based on frontier-based approach and Fisher Information Matrix for autonomous exploration

This paper presents a multi-objective exploration strategy for autonomous exploration of unknown indoor environments. The strategy mainly consists of two parts. First, it evaluates and determines the best frontier by considering the sensor information, localizability and navigation distance simultaneously. Second, a motion planning method considering the robot uncertainty is used to generate trajectories towards selected frontier. Compared to other exploration algorithms, we pay much attention to how to ensure accurate localization during the exploration and motion planning, which means the strategy should select frontiers and generate trajectories that provide sufficient information to keep the robot well-localized. Simulation experiments are presented to show the feasibility of the proposed strategy.

[1]  Kristine L. Bell,et al.  A Lower Bound on the Estimation Error for Markov Processes , 2007 .

[2]  Andrea Censi,et al.  On achievable accuracy for pose tracking , 2009, 2009 IEEE International Conference on Robotics and Automation.

[3]  Yong Wang,et al.  Localizability estimation for mobile robots based on probabilistic grid map and its applications to localization , 2012, 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[4]  Oliver Brock,et al.  Sampling-Based Motion Planning With Sensing Uncertainty , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[5]  Nicholas Roy,et al.  RANGE - robust autonomous navigation in GPS-denied environments , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Wang Yong,et al.  Probabilistic Grid Map Based Localizability Estimation for Mobile Robots , 2013 .

[7]  David Filliat,et al.  Combined Vision and Frontier-Based Exploration Strategies for Semantic Mapping , 2011, CAR 2011.

[8]  Alan C. Schultz,et al.  Mobile robot exploration and map-building with continuous localization , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[9]  Giuseppe Oriolo,et al.  Frontier-Based Probabilistic Strategies for Sensor-Based Exploration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Andrea Censi,et al.  On achievable accuracy for range-finder localization , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[11]  Wolfram Burgard,et al.  Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Alexei Makarenko,et al.  An experiment in integrated exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Sven Behnke,et al.  Evaluating the Efficiency of Frontier-based Exploration Strategies , 2010, ISR/ROBOTIK.

[14]  Steven M. LaValle,et al.  Computing Smooth Feedback Plans Over Cylindrical Algebraic Decompositions , 2006, Robotics: Science and Systems.

[15]  Lydia E. Kavraki,et al.  Sampling-based motion planning with temporal goals , 2010, 2010 IEEE International Conference on Robotics and Automation.

[16]  Nicholas Roy,et al.  Planning in information space for a quadrotor helicopter in a GPS-denied environment , 2008, 2008 IEEE International Conference on Robotics and Automation.

[17]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[18]  Sebastian Thrun,et al.  Exploration in active learning , 1998 .

[19]  Giuseppe Oriolo,et al.  A Bayesian framework for optimal motion planning with uncertainty , 2008, 2008 IEEE International Conference on Robotics and Automation.

[20]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.